Pressure sensing recognition of FBG array based on random forest algorithm

Abstract

In order to improve the precision of static load pressure recognition and identify the position of the applied force accurately, a fiber Bragg grating (FBG) flexible sensor array is proposed in this work. Numerical analysis for the package thickness (4 mm) and package position (2 mm from the bottom) of the FBG flexible sensor is performed using COMSOL, and optimal package thickness (4 mm) and package position (2 mm from the bottom) are selected in the analysis. By using 12-FBGs layout method and random forest algorithm, the position and load prediction model is established. The results show that the average error of the distance between the prediction points of coordinates X-Y and static load F and the real sample points is 0.092. Finally, to verify the proposed models, the pressure sensing experiments of the flexible FBG array are carried out on this basis. The weights of 100 g to 1 000 g are applied to different regions of the flexible sensor array one by one in accordance with a certain trajectory. The variation of each FBG wavelength was taken as the input of the stochastic forest prediction model, and the coordinate position and the static load size F were taken as the output to establish the prediction model. The minimum distance error between the actual point and the predicted point was calculated by experiment as 0.034 91. The maximum is 0.248 1, and the mean error is 0.151 5. It is concluded that the random forest prediction model has a good prediction effect on the pressure sensing of the flexible FBG sensing array.

References

[1] ZHAO Y, WANG T, ZHAO Z. Track-etch membranes as tools for template synthesis of highly sensitive pressure sensors[J]. ACS applied materials & interfaces, 2022, (1): 14.

[2] ZhuY Z, HartelM C, YuN, et al.. Epidermis-inspired wearable piezoresistive pressure sensors using reduced graphene oxide self-wrapped copper nanowire networks[J]. Small methods, 2021, 6(1):2100900

[3] YuC, DanW C, ShaB, et al.. Highly stretchable triboelectric tactile sensor for electronic skin[J]. Nano energy, 2019, 64: 103907

[4] WangC, LiX, GaoE, et al.. Wearable strain sensors: carbonized silk fabric for ultrastretchable, highly sensitive, and wearable strain sensors[J]. Advanced materials, 2016, 28(31): 6639-6639

[5] ZhangY, YeJ, LinZ, et al.. A piezoresistive tactile sensor for a large area employing neural network[J]. Sensors, 2019, 19(1):27-43

[6] FengZ, ZhaoC, et al.. Flexible piezoelectric-induced pressure sensors for static measurements based on nanowires/graphene heterostructures[J]. ACS nano, 2017, 11(5):4507-4513

[7] YxaB, YsaC, LanT A, et al.. A flexible, ultra-highly sensitive and stable capacitive pressure sensor with convex microarrays for motion and health monitoring-ScienceDirect[J]. Nano energy, 2020, 70: 104436

[8] ZhangY T, JiangY, CuiY, et al.. Research on the micro optical fiber Fabry-Perot ultra-low temperature pressure sensor[J]. Acta photonica sinica, 2022, 51(05):229-235(in Chinese)

[9] SosammaS, AnishK, ChandanK M. Fiber Bragg grating tactile sensor for imaging[J]. Optik, 2019, 198: 163062

[10] XiongZ L, LiangD K, LiT W, et al.. Impact localization by using fiber Bragg grating sensors based on correlation dimension[J]. Chinese journal of lasers, 2016, 43(08):203-211

[11] ChenS B, PanX W, LiuJ F. Impact localization method based on the partial least squares regression fractal dimension[J]. Journal of vibration and shock, 2021, 40(02):97-102

[12] WangW, TaoC Y, ZhuY Q, et al.. Application of fiber Bragg grating sensor array in impact location monitoring[J]. Chinese journal of scientific instrument, 2022, 43(06):76-82

[13] ZhangX X, SongY M, MengF Y, et al.. Flexible composite skin embedded optical fiber shape sensing for variant aircraft[J]. Infrared and laser engineering, 2019, 48(06):393-400

[14] WangY, QinN, LiuJ H, et al.. Performance test of temperature and pressure flexible sensor based on optical fiber bragg grating[J]. Chinese journal of scientific instrument, 2019, 40(03):93-98

[15] GeZ Y, WangY, QinN, et al.. Analysis of strain error of FBG flexible sensor with relative slip under vertical pressure[J]. Acta photonica sinica, 2020, 49(10):43-51(in Chinese)

[16] JinP, WangY, QinN, et al.. Relative slipping at the interfaces of the flexible fiber Bragg grating sensor[J]. Optoelectronics letters, 2021, 17(10):604-610

[17] HeS J, LiuD T, PengY. Flight mode recognition method of the unmanned aerial vehicle based on telemetric data[J]. Chinese journal of scientific instrument, 2016, 37(09):2004-2013

[18] FangX R, WenZ F, ChenJ L, et al.. Remote sensing estimation of suspended sediment concentration based on random forest regression model[J]. Journal of remote sensing, 2019, 23(4):756-772